Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Point-Based Planning for Multi-Objective POMDPs
Authors: Diederik Marijn Roijers, Shimon Whiteson, Frans A. Oliehoek
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that OLSAR outperforms, both in terms of runtime and approximation quality, alternative methods and a variant of OLSAR that does not leverage reuse. |
| Researcher Affiliation | Academia | 1Informatics Institute, University of Amsterdam, The Netherlands 2Department of Computer Science, University of Liverpool, United Kingdom |
| Pseudocode | Yes | Algorithm 1: OLSAR(b0, η) and Algorithm 2: OCPerseus(A, B, w, η) |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper uses benchmark POMDP problems (Tiger, Maze20) and generates 'sampled beliefs' for its experiments. It does not provide concrete access information (link, DOI, citation) for a publicly available, pre-existing dataset that is 'trained' on in the traditional sense. |
| Dataset Splits | No | The paper mentions generating a 'reference set' for comparison, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or counts of a fixed dataset) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper describes the algorithms and their implementation details but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We ran all algorithms with 100 belief points generated by random exploration, η = 1 × 10−6, and b0 set to a uniform distribution. |